Linear-frequency-modulation (LFM) signals have been used for a long time as search waveforms for sonar systems. One application of LFM is the continuous transmission frequency modulation (CTFM) sonar which has been used for fish finding and obstacle avoidance. In the CTFM sonar, a very long LFM sweep is transmitted continuously. The angular sweep of a receiving hydrophone gives the echo from the target a pulse length equal to the time it takes the beamwidth of the receiving hydrophone to sweep past the target. The echo is mixed with the transmitted sweep and the difference frequency, which is constant, is detected. This difference frequency, which is a demodulated target echo, determines the range to the target.
LFM and other forms of FM pulses have also been used in active sonar applications. Active sonars have been developed that use weighted sums of discrete element outputs from receiving arrays for detecting targets. Adaptive beamforming has been applied to active signals, though it is more commonly used in passive receiving applications to weight the receiving elements to provide an improved coherent beam output. The prior art applications of adaptive beamforming to active sonar have a problem when they use the frequency domain approach for their adaptive beamforming. The problem is that they have to deal with a block size of data that is at least twice as long as the time required for a signal to fill (completely envelope) the entire array of receiving elements. This time may be very long compared to the inherent time resolution of the waveform which is the reciprocal of bandwidth (1/B). When the adaptive beamforming is done, weights are derived that seek to null out sources of interference that arrive at the same time as a given range resolution cell. However, instead of dealing with sources of interference that are active very close to a time resolution cell (1/B in length) under consideration, they deal with sources of interference which may be miles away. This is because they are processing data over a block of time which is long enough to make sure that the array has been filled. Consequently, the prior art is controlled by the size of the array relative to 1/B. The longer the array, the larger the block of data they have to deal with and hence the further they are driven away from dealing with local parameters at any range from the array. A further issue has to do with waveforms that have a large time-bandwidth product and must be subjected to some kind of pulse compression, such as correlation processing, to achieve the 1/B time resolution. Prior to pulse compression, any sample of the received signal contains echoes form the entire pulse length, which is much longer than 1/B. Hence, the prior art is also restricted in it's ability to deal with local parameters by the pulse compression requirement.